Front. Anim. Sci. Frontiers in Animal Science Front. Anim. Sci. 2673-6225 Frontiers Media S.A. 10.3389/fanim.2026.1744414 Systematic Review Bovine mastitis-associated bacterial pathogens: a systematic review based on some African dairy systems GumedeLungile 1 Writing – original draft Writing – review & editing RamuadaMpho 1 Writing – original draft Writing – review & editing TyasiThobela Louis 1 Writing – original draft Writing – review & editing ChituraTeedzai 2 * Writing – original draft Writing – review & editing Department of Agricultural Economics and Animal Production, Faculty of Science and Agriculture, University of Limpopo, Sovenga, South Africa Department of Animal Science, University of Venda, Thohoyandou, South Africa Correspondence: Teedzai Chitura, teedzai.chitura@univen.ac.za 27 02 2026 2026 7 1744414 11 11 2025 26 01 2026 20 01 2026 Copyright © 2026 Gumede, Ramuada, Tyasi and Chitura. 2026 Gumede, Ramuada, Tyasi and Chitura https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Mastitis occurs when microbes invade the teat through the teat canal. Most microbes can cause opportunistic infections of the udder. However, fatal infections are due to various species of Streptococci, Staphylococci, and coliforms. Notable economic effects of bovine mastitis include reduced milk yield, increased treatment costs, and premature culling of affected animals. This systematic review collates published reports of bacterial pathogens isolated from mastitis-positive bovine milk samples in African dairy systems from 2013 to 2023. The search was conducted using Google Scholar, PubMed, Web of Science, and ScienceDirect, and assessed using a modified ROBIS tool. The search terms were: “bovine mastitis”, “bacterial pathogens”, and “African dairy systems”. Findings indicate that Staphylococcus aureus (80%) and Escherichia coli (63%) were among the most frequently reported pathogens. Other frequently reported bacterial species included Streptococcus spp. (37%) and Streptococcus agalactiae (37%). A greater proportion of the studies were based on Ethiopian dairy farming systems. Overall, the results show that mastitis-causing pathogens are common isolates in milk from smallholder African farming systems. Therefore, milk bacterial isolation and testing should be adopted as a standard practice to inform decision-making on mastitis treatment and control programmes.

 bacterial isolation Escherichia coli Staphylococcus species Streptococcus species udder infections The author(s) declared that financial support was received for this work and/or its publication. The authors acknowledge the University of Limpopo and the University of Venda for providing the financial assistance towards the publication of the study. section-at-acceptance Animal Physiology and Management Introduction

Mastitis results in reduced milk yield and quality, compromises animal welfare, and increases the rate of premature culling of cows, and increases the running costs incurred by farmers when treating infected animals. The disease limits farmer income in production systems that are often small-scale and subsistence-based (Ajose et al., 2022; Crippa et al., 2024). Zoonotic pathogens such as S. aureus and E. coli remain problematic, especially in informal milk markets where surveillance systems to detect and monitor the emergence of pathogens at the human-animal interface are weak or absent (Garcia et al., 2019). In addition, the misuse of antimicrobials when managing mastitis-positive cases contributes to drug residues and resistant bacteria that enter the environment through milk waste, sustaining reservoirs of infection that may yield mixed infections (van Zyl et al., 2023; Lyimo and Sonola, 2025). Environmental pathogens include Escherichia coli and Klebsiella species, which are typically derived from the cow’s surroundings (Phiri et al., 2022; van Zyl et al., 2023; Khasaphane et al., 2023). Hota et al. (2020); Yusuf-Isleged (2022); and Ramuada et al. (2024) highlighted the type of production system, dairy cattle breed, seasonal changes, and hygiene practices during milking among the various factors that can influence the prevalence of bovine mastitis as well as the microbiota of the udder. Additionally, the scarcity of water around the animal production facilities, as well as the lack of awareness among farmers, were reported to play a significant role. Previous studies emphasised the importance of understanding breed susceptibility, production systems, and seasonal influences on mastitis prevalence and pathogen diversity (Moosavi et al., 2014; Cremonesi et al., 2018). In the African dairy farming systems, particularly the smallholder farming sector, mastitis has a high incidence rate and therefore is a significant economic burden to the farmers. Therefore, the study systematically collates literature on the bacterial pathogens that were isolated from milk samples of mastitis-positive cows raised in diverse African production systems.

 Materials and methods Eligibility criteria

Identification of Population, Exposure, and Outcomes (PEO) components was performed for this systematic review. PEO ensures clarity in defining the research question (Capili, 2020). The “dairy cows” were defined as the population of the study, with “mastitis-causing bacterial pathogens” as the exposure and “reported prevalence and diversity of pathogens in African dairy systems” as the outcomes. Before conducting the review, an initial search of the PEO elements was performed on Google Scholar, PubMed, Science Direct, and Web of Science.

 Search strategy

Two authors independently conducted a systematic review of articles in the databases Google Scholar, PubMed, Science Direct, and Web of Science, using combinations of the following key terms: ‘bovine mastitis’, “bacterial pathogens”, and “African dairy systems”. Search terms were combined using Boolean operators (AND, OR) and adapted for each database. The manual search yielded 113 full research articles from the databases.

 Inclusion criteria

This systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Moher et al., 2009). Following these guidelines ensures that all pertinent information is incorporated into the analysis. After screening, 83 articles were eliminated, resulting in the inclusion of 30 studies in this systematic review based on the following criteria: (i) Focus on bovine mastitis, (ii) Availability of total sample size data, (iii) Access to full-text articles, (iv) Peer-reviewed status, (v) Publication date between 1 January 2013 until 31st December 2023, (vi) Conducted within Africa, (vii) Addressing mastitis pathogens. Quality assessment was performed using a simplified ROBIS tool applied to all 30 included studies (Whiting et al., 2016). Five domains were evaluated: (i) clarity of inclusion criteria, (ii) description of diagnostic methods, (iii) appropriateness of sampling, (iv) clarity of pathogen reporting, and (v) consideration of confounding factors. Each domain was scored as low or high risk of bias. A total score out of 5 was used to categorise each study as low (4–5), moderate (3), or high (0–2) risk of bias.

 Exclusion criteria

Studies were excluded if they (i) were not published in English, (ii) were review articles, (iii) were theses or dissertations, (iv) were studied with no clearly defined number of samples screened, (v) the study area was not mentioned, (vi) had no isolates, or (vii) had no number of isolates. (viii) outside the year range 2013 to 2023.

 Data extraction

Two authors independently extracted data and resolved discrepancies by consensus. Extracted data included author names, publication year, location, total number of isolates, frequency percentages, and pathogens isolated. Data were compiled into an Excel spreadsheet (Microsoft Corporation, 2024). Text, tables, and figures were used for data extraction and presentation.

 Data analysis

Pathogen detection was synthesised at the study level and computed pathogen-specific pooled prevalence as a sample size-weighted proportion across studies. Pooled prevalence for mastitis positive samples was calculated as:

Pooled prevelance %=∑positive samples∑total samples *100 

To address heterogeneity, season was harmonised as either wet, dry, or wet-to-dry or not reported. Some studies collected data across both seasons; classification was derived based on the month reported in the study, and seasonal variations were noted per country and classified. Production system was harmonised as intensive, semi-intensive or extensive or not reported; and breed as exotic, crossbred, indigenous or not reported. Missing data were retained as not reported for consistency. Sensitivity analyses were performed by (i) excluding studies at high risk of bias and (ii) excluding the largest denominator study to assess the effect of outlier sample size on pooled estimates.

 Results Searched results

Figure 1 characterises the flowchart of the identification and selection of studies for the systematic review. The search yielded 113 full-text articles. After removing duplicates and applying eligibility criteria, 30 studies were included in the systematic review. Reasons for exclusion included: studies outside the 2013–2023 range, non-African regions, non-bovine species, review articles, theses, and studies lacking isolate data.

Literature search and selection process following the PRISMA procedure.

Flowchart detailing the selection process for a review: 113 articles were identified from four sources, with 4 duplicates removed, leaving 109 articles screened. Exclusions included: outside the 2013-2023 range (18), not in Africa (4), not bovine-related (13), review articles (10), and thesis/abstracts (8). 56 articles were sought and assessed for eligibility. Exclusions at this stage included studies on pathogen genotyping, milk quality, or indefinite sample numbers (26). Finally, 30 articles were included in the review. Characteristics of included studies

A total of 30 included studies represented diverse African systems. Using a ROBIS-style 5 domain assessment applied to the 30 included studies, 17 studies were judged to have low overall risk of bias, 9 had moderate risk, and 4 were classified as high risk. High-risk studies commonly exhibited failure to account for potential confounding factors such as production system, breed, season, or outlying sample size. Diagnostic methods were generally robust across the studies, with all investigations using conventional bacteriological culture for pathogen identification. Screening approaches varied between California Mastitis Test (n = 26), Electric conductivity (n = 1), Somatic Cell Count (n =1), pH testing (n =1), and physical examinations (n =1). Therefore, all studies were rated low risk in the diagnostic methods domain across the included studies.

The combined sample size of mastitis-positive cases across the 30 studies was 35672, and the sample population was 101877. The pooled prevalence was based on the positive milk samples across all studies, indicating a pooled mastitis prevalence of 35%. Data analysis based on Table 1 showed that crossbreeds were the most frequently reported bovine breed (n = 12). Regarding production systems, intensive systems were the most studied (n = 11), whereas extensive systems were the least represented (n = 5). Crossbred cattle were the most studied (n = 12), followed by exotic cattle (n = 6), while indigenous cattle were the least reported (Amdhun et al., 2016; Kitila et al., 2021; Byaruhanga et al., 2022). Nine studies did not specify the breed of cattle. Most studies were conducted during the dry season (n =13), followed by the wet-to-dry transition (n =12); the fewest number of studies were conducted during the wet season (Zeryehun et al., 2013; Abrahmsén et al., 2014). Figure 2 shows that the highest number of articles was published in 2013 (n = 6), followed by 2017 (Adane et al., 2017; Beyene et al., 2017; Birhanu et al., 2017; Mekonnen et al., 2017; Petzer et al., 2017). Figure 3 depicts the geographical distribution of the 30 reviewed studies. Ethiopia had the highest number of studies (n = 13), followed by Uganda (Kateete et al., 2013; Ssajjakambwe et al., 2017; Byaruhanga et al., 2022). While Sudan had the lowest (Salih, 2015).

Characterisation of the included studies.

Author Production system Breed Season Country Positive samples Culture positive
Kateete et al. (2013) Semi-intensive Exotic Wet-Dry Uganda 97 82
Zeryehun et al. (2013) Intensive Crossbreed Wet Ethiopia 499 80
Gelgelu et al. (2023) Semi-intensive Crossbreed Wet-Dry Ethiopia 344 302
Kashoma et al. (2015) Intensive Not mentioned* Dry Tanzania 100 49
Salih (2015) Semi-intensive Not mentioned* Dry Sudan 500 100
Seid et al. (2015) Semi-intensive Crossbreed Dry Ethiopia 101 83
Ssajjakambwe et al. (2017) Intensive Crossbreed Not mentioned* Uganda 71 65
Amdhun et al. (2016) Extensive Indigenous Dry Ethiopia 1536 119
Adane et al. (2017) Intensive Crossbreed Dry Ethiopia 384 35
Yohannes and Alemu (2018) Not mentioned* Exotic breed Dry Ethiopia 173 51
Ngwa et al. (2018) Semi-intensive Crossbreed Wet-Dry Cameroon 164 68
Kaki et al. (2019) Intensive Not mentioned* Dry Algeria 100 26
Belay et al. (2022) Intensive & Semi-intensive Cross breed Wet-Dry Ethiopia 72 64
Abegewi et al. (2022) Intensive Cross breed Wet-Dry Cameroon 205 132
Ngotho et al. (2022) Intensive Exotic breed Wet-Dry Kenya 140 101
Byaruhanga et al. (2022) Extensive Indigenous breed Dry Uganda 1152 253
Fosgate et al. (2013) Not mentioned* Not mentioned* Not mentioned* South Africa 484 477
Gitau et al. (2013) Intensive & Semi-intensive Not mentioned* Dry Kenya 269 241
Abrahmsén et al. (2014) Extensive Exotic breed Wet Uganda 195 168
Beyene et al. (2017) Not mentioned* Not mentioned* Dry Ethiopia 92 24
Mekonnen et al. (2017) Intensive Cross breed Not mentioned* Ethiopia 1543 633
Birhanu et al. (2017) Intensive Cross breed Dry Ethiopia 170 153
Petzer et al. (2017) Not mentioned* Not mentioned* Wet-Dry South Africa 89635 30399
Saidi et al. (2013) Semi extensive Not mentioned* Wet-Dry Algeria 428 48
Suleiman et al. (2018) Not mentioned* Not mentioned* Dry-Wet Tanzania 1648 831
Ndahetuye et al. (2019) Not mentioned* Exotic breed Wet-Dry Rwanda 418 291
Ndahetuye et al. (2020) Extensive Exotic breed Wet-Dry Rwanda 664 457
Kitila et al. (2021) Extensive Indigenous breed Dry Ethiopia 210 153
Demil et al. (2022) Intensive Cross breed Dry Ethiopia 419 128
Abebe et al. (2023) Intensive Cross breed Dry-Wet Ethiopia 64 59

*Not mentioned: information was not provided in the primary article.

Categorisation of the publications by year.

Bar chart showing the number of publications per year from 2013 to 2023. 2013 and 2022 have the highest count at six. Other years vary between zero and five publications.

Geographical distribution of publications.

Map of Africa highlighting countries with numbers. Uganda, Kenya, and Rwanda show 3 and 2 respectively. Ethiopia has 13. Tanzania, Cameroon, Algeria, and South Africa show 2 each. South Sudan indicates 1. These numbers are in a legend with corresponding colors. Bovine mastitis isolated pathogens

Table 2 shows a total of 74 unique mastitis pathogens, including co-infections and coliforms, which were identified across the 30 reviewed articles. Staphylococcus aureus was the most frequently isolated pathogen (n = 24), accounting for 80% of the total studies reviewed. Staphylococcus aureus was commonly reported during the dry season (n = 10) and during the wet-to-dry seasons (n = 10), with the wet season being the least reported (Zeryehun et al., 2013). Fosgate et al. (2013); Mekonnen et al. (2017) did not mention the seasonal variations of the study site. Among the 30 reviewed studies, crossbred cattle were the most frequently reported as susceptible to S. aureus (n = 10), followed by exotic cattle (n = 6), while indigenous cattle were the least reported (Amdhun et al., 2016; Byaruhanga et al., 2022). Eight studies did not specify the breed of cattle. Most studies reported on S. aureus and co-infections involving S. aureus isolations were in intensive and semi-intensive production systems (n = 7; n =7), with the fewest reports from extensive systems (Abrahmsén et al., 2014; Amdhun et al., 2016; Ndahetuye et al., 2020; Byaruhanga et al., 2022). However, some studies (n = 6) did not specify the type of production system in which S. aureus was reported.

Bovine mastitis-isolated pathogens.

ISOLATE AUTHOR ISOLATE AUTHOR ISOLATE AUTHOR
1. Staphylococcus aureus Fosgate et al. (2013); Gitau et al. (2013); Kateete et al. (2013); Saidi et al. (2013); Zeryehun et al. (2013); Abrahmsén et al. (2014); Kashoma et al. (2015); Seid et al. (2015); Amdhun et al. (2016); Beyene et al. (2017); Birhanu et al. (2017); Mekonnen et al. (2017); Petzer et al. (2017); Ngwa et al. (2018); Suleiman et al. (2018); Yohannes and Alemu (2018); Kaki et al. (2019); Ndahetuye et al. (2019); Ndahetuye et al. (2020); Belay et al. (2022); Byaruhanga et al. (2022); Demil et al. (2022); Abebe et al. (2023); Gelgelu et al. (2023) 2. Escherichia coli Fosgate et al. (2013); Kateete et al. (2013); Saidi et al. (2013); Zeryehun et al. (2013); Abrahmsén et al. (2014); Seid et al. (2015); Amdhun et al. (2016); Adane et al. (2017); Birhanu et al. (2017); Petzer et al. (2017); Ssajjakambwe et al. (2017); Ngwa et al. (2018); Suleiman et al. (2018); Yohannes and Alemu (2018); Kaki et al. (2019); Abegewi et al. (2022); Belay et al. (2022); Ngotho et al. (2022); Abebe et al. (2023); Gelgelu et al. (2023) 3. Other Streptococcus spp. Gitau et al. (2013); Kateete et al. (2013); Saidi et al. (2013); Abrahmsén et al. (2014); Seid et al. (2015); Adane et al. (2017); Birhanu et al. (2017); Kaki et al. (2019); Abebe et al. (2023); Belay et al. (2022); Byaruhanga et al. (2022)
4. Streptococcus agalactiae Fosgate et al. (2013); Gitau et al. (2013); Zeryehun et al. (2013); Seid et al. (2015); Amdhun et al. (2016); Mekonnen et al. (2017); Petzer et al. (2017); Ngwa et al. (2018); Ndahetuye et al. (2019); Demil et al. (2022) 5. coagulase-negative Staphylococcus Fosgate et al. (2013); Gitau et al. (2013); Abrahmsén et al. (2014); Mekonnen et al. (2017); Petzer et al. (2017); Ngwa et al. (2018); Byaruhanga et al. (2022); Abebe et al. (2023) 6. Micrococcus spp. Fosgate et al. (2013); Zeryehun et al. (2013); Mekonnen et al. (2017); Petzer et al. (2017); Suleiman et al. (2018); Yohannes and Alemu (2018); Kitila et al. (2021); Gelgelu et al. (2023)
7. Other Klebsiella spp. Gitau et al. (2013); Salih (2015); Ssajjakambwe et al. (2017); Suleiman et al. (2018); Ngotho et al. (2022); Abebe et al. (2023); Gelgelu et al. (2023) 8. Other Staphylococcus spp. Gitau et al. (2013); Salih (2015); Adane et al. (2017); Birhanu et al. (2017); Ssajjakambwe et al. (2017); Kitila et al. (2021); Ngotho et al. (2022) 9. Bacillus spp. Salih (2015); Mekonnen et al. (2017); Ssajjakambwe et al. (2017); Suleiman et al. (2018); Abebe et al. (2023); Gelgelu et al. (2023)
10. Corynebacterium bovis Gitau et al. (2013); Mekonnen et al. (2017); Yohannes and Alemu (2018); Ngwa et al. (2018); Byaruhanga et al. (2022) 11. Streptococcus dysgalactiae Fosgate et al. (2013); Mekonnen et al. (2017); Petzer et al. (2017); Ngwa et al. (2018); Yohannes and Alemu (2018) 12. Other Corynebacterium spp. Salih (2015); Ssajjakambwe et al. (2017); Suleiman et al. (2018); Abebe et al. (2023); Gelgelu et al. (2023)
13. Klebsiella pneumoniae Seid et al. (2015); Petzer et al. (2017); Yohannes and Alemu (2018); Abegewi et al. (2022); Belay et al. (2022) 14. Mixed growth Fosgate et al. (2013); Gitau et al. (2013); Saidi et al. (2013); Abrahmsén et al. (2014); Kaki et al. (2019) 15. Streptococcus uberis Fosgate et al. (2013); Mekonnen et al. (2017); Petzer et al. (2017); Ndahetuye et al. (2019); Ndahetuye et al. (2020)
16. non-aureus Staphylococci Ndahetuye et al. (2019); Ndahetuye et al. (2020); Belay et al. (2022); Abebe et al. (2023) 17. Klebsiella oxytoca Kateete et al. (2013); Saidi et al. (2013); Ndahetuye et al. (2019); Abegewi et al. (2022) 18. Other Proteus spp. Ssajjakambwe et al. (2017); Suleiman et al. (2018); Abebe et al. (2023); Gelgelu et al. (2023)
19. Other Pseudomonas spp. Saidi et al. (2013); Ssajjakambwe et al. (2017); Kaki et al. (2019); Ngotho et al. (2022) 20. Pseudomonas aeruginosa Zeryehun et al. (2013); Seid et al. (2015); Suleiman et al. (2018); Byaruhanga et al. (2022) 21. Staphylococcus intermedius Beyene et al. (2017); Birhanu et al. (2017); Yohannes and Alemu (2018); Gelgelu et al. (2023)
22. Other Enterobacteriae spp. Fosgate et al. (2013); Kaki et al. (2019); Kitila et al. (2021) 23. Other Enterobacter spp. Amdhun et al. (2016); Suleiman et al. (2018); Abebe et al. (2023) 24. Enterococcus faecalis Fosgate et al. (2013); Petzer et al. (2017); Ngwa et al. (2018)
25. Other Serratia spp. Petzer et al. (2017); Ssajjakambwe et al. (2017); Suleiman et al. (2018) 26. Staphylococcus epidermidis Amdhun et al. (2016); Suleiman et al. (2018); Yohannes and Alemu (2018) 27. Staphylococcus hyicus Beyene et al. (2017); Birhanu et al. (2017); Yohannes and Alemu (2018)
28. Coliforms Mekonnen et al. (2017); Byaruhanga et al. (2022) 29. Trueperella pyogenes Petzer et al. (2017); Ngwa et al. (2018); Suleiman et al. (2018) 30. Citrobacter freundii Kateete et al. (2013); Abegewi et al. (2022)
31. Clostridium spp. Mekonnen et al. (2017); Suleiman et al. (2018) 32. Enterobacter cloacae Saidi et al. (2013); Abegewi et al. (2022) 33. Klebsiella variicola Ndahetuye et al. (2019); Ndahetuye et al. (2020)
34. Pasteurella spp. Suleiman et al. (2018); Gelgelu et al. (2023) 35. Lactococcus spp. Kateete et al. (2013); Ndahetuye et al. (2020) 36. Pseudomonas fluorescens Suleiman et al. (2018); Ndahetuye et al. (2019)
37. Serratia odorifera Saidi et al. (2013); Abegewi et al. (2022) 38. Arcanobacterium Kateete et al. (2013) 39. Acinetobacter iwofii Ndahetuye et al. (2019)
40. Cedecea davisae Kateete et al. (2013) 41. Campylobacter spp. Suleiman et al. (2018) 42. Chromobacterium violaceum Suleiman et al. (2018)
43. Enterobacter asburiae Ndahetuye et al. (2020) 44. Enterobacter sakazakii Abegewi et al. (2022) 45. Enterococcus canis Petzer et al. (2017)
46. Lactobacillus spp. Ssajjakambwe et al. (2017) 47. Enterococcus faecium Ndahetuye et al. (2019) 48. Leclercia adecarboxylata Kateete et al. (2013)
49. Negative growth Abrahmsén et al. (2014) 50. Neisseria spp. Suleiman et al. (2018) 51. Other Enterococcus spp. Mekonnen et al. (2017)
52. Proteus vulgaris Kateete et al. (2013) 53. Pneumotropica haemolytica Saidi et al. (2013) 54. Providencia alcalifaciens Saidi et al. (2013)
55. Providencia stuartii Saidi et al. (2013) 56. Pseudomonas rhodesiae Ndahetuye et al. (2019) 57. Rhodococcus equi Kitila et al. (2021)
58. Salmonella spp. Belay et al. (2022) 59. Serratia ficaria Abegewi et al. (2022) 60. Serratia liquefaciens Abegewi et al. (2022)
61. Serratia marcescens Kateete et al. (2013) 62. Staphylococcus haemolyticus Suleiman et al. (2018) 63. Staphylococcus pseudintermedius Petzer et al. (2017)
64. Staphylococcus warneri Saidi et al. (2013) 65. Staphylococcus xylosus Saidi et al. (2013) 66. Streptococcus faecalis Zeryehun et al. (2013)
67. Streptococcus pyogenes Petzer et al. (2017) 68. Staphylococci + Streptococcus spp. Gitau et al. (2013); Kaki et al. (2019) 69. Streptococcus spp.+ Staphylococcus aureus + Escherichia coli Kaki et al. (2019)
70. Staphylococcus aureus + Escherichia coli Kaki et al. (2019) 71. Staphylococcus aureus + Mycoplasma spp. Kaki et al. (2019) 72. Staphylococcus aureus + Streptococcus spp. Kaki et al. (2019)
73. Staphylococcus lentus + Klebsiella ornithinolytica Saidi et al. (2013) 74. Streptococcus spp. + Escherichia coli Kaki et al. (2019)

Escherichia coli (E. coli) was the second most frequently reported pathogen (n = 20), accounting for63% of the total studies. E. coli and co-infections involving E. coli were commonly reported in intensive production systems (n = 8), followed by semi-intensive systems (n = 6), with the lowest occurrence in extensive systems (Abrahmsén et al., 2014; Amdhun et al., 2016). Four studies that reported on E. coli did not specify the type of production system (Fosgate et al., 2013; Petzer et al., 2017; Suleiman et al., 2018; Yohannes and Alemu, 2018). E. coli isolates were most frequently reported in cross breeds (n = 10), followed by exotic breeds (Kateete et al., 2013; Abrahmsén et al., 2014; Yohannes and Alemu, 2018; Ngotho et al., 2022), with indigenous breeds being the least reported (Amdhun et al., 2016). Several studies reported on E. coli without specifying the breed (Fosgate et al., 2013; Saidi et al., 2013; Petzer et al., 2017; Suleiman et al., 2018; Kaki et al., 2019). Studies reporting E. coli and co-infections involving E. coli were most frequently conducted across both the dry and wet seasons (n = 10), followed by the dry season (n = 6). Only two studies were conducted during the wet season (Zeryehun et al., 2013; Abrahmsén et al., 2014), while two studies did not specify the season (Fosgate et al., 2013; Ssajjakambwe et al., 2017).

Streptococcus agalactiae (S. agalactiae) (n = 11) and other unspecified Streptococcus spp. were the third most reported isolates (n = 11), accounting for 37% of the total reviewed studies. Studies that reported S. agalactiae isolates during the dry season were most common (Gitau et al., 2013; Seid et al., 2015; Amdhun et al., 2016; Yohannes and Alemu, 2018; Demil et al., 2022), followed by studies conducted during both the dry and wet seasons (Petzer et al., 2017; Ndahetuye et al., 2019; Ngwa et al., 2018). This pathogen was least reported during the wet season (Zeryehun et al., 2013), whilst two studies did not mention the seasonal variations (Fosgate et al., 2013; Mekonnen et al., 2017). Streptococcus agalactiae isolates were common in crossbreeds (Zeryehun et al., 2013; Seid et al., 2015; Mekonnen et al., 2017; Ngwa et al., 2018; Demil et al., 2022). Indigenous breed was mentioned once in all the reviewed studies (Amdhun et al., 2016), and exotic breeds were mentioned twice (Yohannes and Alemu, 2018; Ndahetuye et al., 2019). Fosgate et al. (2013); Gitau et al. (2013), and Petzer et al. (2017) did not mention breeds that were included in the studies. Streptococcus agalactiae was mostly prevalent in the intensive production system (Zeryehun et al., 2013; Mekonnen et al., 2017; Demil et al., 2022), followed by the semi-intensive (Gitau et al., 2013; Seid et al., 2015; Ngwa et al., 2018), with the least prevalence being in the extensive system (Amdhun et al., 2016). However, Fosgate et al. (2013); Mekonnen et al. (2017); Petzer et al. (2017), and Ndahetuye et al. (2019) did not mention the production system type.

The distribution of key mastitis pathogens is highlighted in Figure 4. S. aureus and E. coli were the most frequently detected pathogens across all strata, while S. agalactiae and other unspecified Streptococcus spp. was less common. Detection of S. aureus and E. coli was highest in intensive and semi-intensive systems and in crossbred cattle. Seasonal analysis showed that S. aureus and E. coli were most often reported in studies conducted during transition periods of wet and dry seasons, whereas S.agalactiae was more frequently detected in dry seasons. Ethiopia accounted for the largest number of detections overall, while Sudan reported none of the four key pathogens of the systematic review and is shown for completeness of the countries involved in the systematic review. Detection counts reflect study-level presence or absence, not isolate counts. Among the key pathogens, S. aureus had the highest pooled prevalence of 21%, followed by S. agalactiae at 6%. E. coli accounted for 1% while other unspecified Streptococci spp. contributed only 0.4%. Excluding high-risk studies did not change the direction of detection patterns; S. aureus and E. coli remained dominant in the intensive systems, and S. agalactiae remained more frequent in the dry season studies.

Detection of four key mastitis pathogens (S. aureus, E. coli, S. agalactiae, Streptococcus spp.) across: (a) Season, (b) Production system, (c) Breed, (d) Country. Bars represent the number of studies reporting each pathogen within each category.

Bar charts displaying the detection of bacterial species across various conditions. Chart a shows detection in different climates: dry, wet, wet-dry, and not reported. Chart b illustrates detection in different farming systems: intensive, semi-intensive, extensive, and not reported. Chart c represents detection in different cattle breeds: crossbreed, exotic, indigenous, and not reported. Chart d compares detection across African countries. Each chart differentiates by species: *S. agalactiae*, *S. aureus*, *Streptococcus spp.*, and *E. coli*, alongside the number of studies.

Co-infections, defined as the isolation of two or more mastitis pathogens from the same sample, were reported in 5 of the 30 reviewed studies and were isolated in 9 of the 74 identified isolates (Gitau et al., 2013; Saidi et al., 2013; Fosgate et al., 2013; Abrahmsen et al., 2014; Kaki et al., 2019). Among these mixed infections, S. aureus was present in four isolates, and E. coli in three isolates (Kaki et al., 2019), and Streptococci were present in four isolates (Gitau et al., 2013; Kaki et al., 2019). The pathogens involved in two identified mixed isolates were not specified (Fosgate et al., 2013; Abrahmsén et al., 2014). Mixed infections were common in both semi-intensive and intensive production systems (Gitau et al., 2013; Saidi et al., 2013; Kaki et al., 2019) and least in the extensive system (Abrahmsén et al., 2014). Most studies that isolated mixed infections did not specify the breed (Fosgate et al., 2013; Gitau et al., 2013; Saidi et al., 2013; Kaki et al., 2019), and one study reported on exotic cattle (Abrahmsén et al., 2014). Two studies reported mixed infections during the dry season (Gitau et al., 2013; Kaki et al., 2019). One study took place during the wet season (Abrahmsén et al., 2014) and one throughout the wet and dry season (Saidi et al., 2013). One study did not mention the seasonal variations (Fosgate et al., 2013). This critical synthesis evaluates the distribution and determinants of bovine mastitis pathogens across 30 studies encompassing 74 unique isolates. The review reveals a consistent dominance of Staphylococcus aureus, Escherichia coli, and Streptococcus agalactiae, with clear associations to production intensity, cattle breed, and seasonal variation. Beyond prevalence, methodological inconsistencies such as incomplete reporting of seasons, production systems, and breeds limit the comparability of findings and highlight key evidence gaps in mastitis epidemiology across sub-Saharan Africa.

 Discussion

The current review showed that the evidence on the geographical distribution of 30 reviewed studies revealed significant regional disparities in mastitis research across African countries. The findings of the study showed that the highest number of reviewed articles were based on milk samples collected from Ethiopian dairy farming systems. This observation could be attributed to the country’s relatively large dairy cattle population and its emerging smallholder-based dairy industry (Dekebo and Kebede, 2023). The concentration of studies in East Africa, contrasted with limited data from regions such as North and Southern Africa, this observation emphasises the urgent need for broader surveillance and more comprehensive research across diverse production systems. Addressing these gaps is crucial for developing effective, evidence-based mastitis control strategies that can reduce economic losses, enhance animal health and productivity, and provide a more comprehensive understanding of mastitis epidemiology across the continent (Mitsunaga et al., 2024).

Over the review period, milk testing and isolation of bacterial pathogens associated with bovine mastitis were performed using various screening methods paired with conventional methods. Although a detailed review of mastitis diagnostic methods falls outside the primary scope of this manuscript, fundamental information on diagnostic approaches used in each included study was extracted only to assess methodological comparability, study quality, and potential sources of heterogeneity in pathogen detection. Sensitivity testing confirmed that major patterns persisted after excluding high-risk studies and outlier denominators, supporting the robustness of directional findings. Although isolation methods were uniform, heterogeneity arose from differences in preliminary screening, sampling frames, and reporting granularity. Harmonisation of season, breed, and production system reduced variability but cannot fully reconcile non-comparable designs; and retaining the Not reported category avoids biased omission, however, it may dilute contrasts.

The dominance of S. aureus confirms its high prevalence in smallholder African dairy farming systems and hence its significant role in contagious mastitis. Sarba and Tola (2017) and Dagnaw et al. (2024) reported that production systems that promote increased animal densities and repeated milking procedures are among the common risk factors for the transmission of contagious pathogens. Azevedo et al. (2016) highlighted that dry seasonal conditions lead to compromised hygiene standards, such as irregular cleaning of the udders, which favours the persistence and spread of contagious pathogens. The ability of S. aureus to form biofilms, produce toxins, and resist common antibiotic therapies enhances the pathogenicity (Wang et al., 2025).

The findings of the study revealed a high occurrence of the E. coli species. However, due to geographical imbalances, one large dataset with a very low E. coli percentage explains the discrepancies between detection frequency and pooled prevalence. Excluding the largest dataset (Petzer et al., 2017) increased E. coli prevalence to 4%, demonstrating the influence of large denominators on weighted estimates. Naranjo-Lucena et al. (2025) indicated that intensive systems and wet-to-dry transition periods are among the factors that promote a high occurrence of E. coli species in milk. The authors further explained that drought-induced hygiene stress and wet-season bacterial proliferation contribute to a high incidence through increased contamination of milking utensils, milking parlour floors, feed, and water, particularly in production facilities where biosecurity and sanitation are suboptimal. Singh (2022) reported the effects of seasonal fluctuations on the prevalence of E. coli species in dairy farming systems. Kitila et al. (2021) and Byaruhanga et al. (2022) highlighted the issue of genetic susceptibility, with some exotic dairy cattle breeds being reported as highly predisposed to mastitis due to their higher milk yield and associated metabolic stress. Ayalew et al. (2023) and Slayi et al. (2024) indicated that the dairy cattle breeds that are indigenous to Africa have developed resilience to mastitis due to their genetic adaptation to African environmental conditions, including resistance to heat stress and endemic pathogens. These findings underscore the need for selecting breeding programs that strike a balance between productivity and disease resistance, potentially integrating genetic traits from indigenous breeds to enhance mastitis resilience in crossbred cattle. However, differences in management practices and access to veterinary care are also critical factors that determine the occurrence and diversity of bacterial pathogens associated with bovine mastitis.

The study revealed Streptococcus species as the third most common bacterial pathogens that were isolated from mastitis-positive milk samples. Blignaut et al. (2018) stated that the control of Streptococcus bacterial species is a challenge, especially in more commercially oriented dairy setups. Streptococci are highly contagious pathogens that are commonly spread during milking and may persist in the mammary gland without showing any clinical signs and thus leading to chronic clinical mastitis, making detection and eradication difficult (Morales-Ubaldo et al., 2023). Previous research has shown that Streptococcus species, particularly S. agalactiae, are mostly reported during the dry seasons, which relates to its subclinical persistence nature, with increased cases likely under more stressful or poor sanitation conditions.

The dominance of S. aureus and Streptococcus spp and S. agalactiae in the reviewed studies confirms their role as primary gram-positive pathogens associated with contagious mastitis. In contrast, E. coli represents a major Gram-negative pathogen commonly linked to environmental mastitis. Mastitis pathogens may be classified as either Gram-positive or Gram-negative bacteria based on their cell wall structures. Classification of mastitis pathogens assists in a more appropriate intervention approach because Gram-negative bacteria are resistant to broad-spectrum antibiotic therapy (Steele et al., 2020). Understanding these differences is essential for designing effective control programs, as treatment protocols targeting gram-positive pathogens may fail against gram-negative pathogens, leading to therapeutic failures and contributing to antimicrobial resistance, which poses a significant one-health risk. Resistant bacteria and drug residues can enter the food chain through contaminated milk and spread to humans, while environmental contamination from discarded milk and manure sustains reservoir organisms. This highlights the need for routine pathogen identification and antimicrobial sensitivity testing before initiating treatment, particularly in resource-limited African dairy systems where empirical therapy is common to safeguard public health and reduce environmental contamination (Marbach et al., 2019; Perdomo et al., 2024).

Multiple bacterial species were isolated from the same milk samples. The occurrence of mixed infections, particularly involving S. aureus, Streptococcus spp., and E. coli, highlights the complexity of mastitis infections and the potential for synergistic pathogen interactions. Shum et al. (2009) stated that the co-isolation of S. aureus and E. coli in mixed infections reinforces the need for differential diagnosis, rather than solely relying on clinical or somatic cell counts, as co-infections may introduce new pathogens and complicate mastitis management. Poor hygiene practices, high stocking densities, and increased cow-to-cow contact in intensive systems may facilitate pathogen transmission and co-infections (Abed et al., 2021). Gitau et al. (2013) and Kaki et al. (2019) indicated that most cases of co-infections are documented during the dry season, suggesting that environmental stressors such as heat, limited water availability, and increased pathogen load in bedding and milking equipment may contribute to higher infection rates. On the other hand, multiple occurrences of milk bacteria could be attributed to improper sampling procedures or contamination (Dyson et al., 2022).

 Conclusion and recommendations

Bovine mastitis is a major challenge in African dairy systems. The systematic collation of potentially zoonotic pathogens in the reviewed studies, such as S. aureus and E. coli, presents a threat to the One Health initiative, especially in informal milk markets, where surveillance systems to detect and monitor the emergence of pathogens at the human-animal interface are weak or absent. There is an urgent need for the responsible use of antimicrobials in mastitis management to curb drug residues and bacterial resistance. The study concludes that effectively addressing mastitis in African dairy systems demands coordinated strategies that integrate animal health, human health, and environmental health within the One Health framework. Such an approach not only improves disease control but also promotes sustainable dairy production and public health resilience.

 Author contributions

TC: Writing – original draft, Writing – review & editing. LG: Writing – original draft, Writing – review & editing. MR: Writing – original draft, Writing – review & editing. TLT: Writing – original draft, Writing – review & editing.

 Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Edited by: Maria Schirone, University of Teramo, Italy

Reviewed by: Ragil Angga Prastiya, Airlangga University, Indonesia

Hongmei Zhao, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, China